Explicación de la tarea 3 Actividad de geometría

This study investigated the relation between symptoms of inattentiveness and hyperactivity based on the CSS self-report behavioral rating scale scores with EEG coherence values across all frequencies in male and female college students. The study was designed to contribute to current literature by examining the relation between ADHD symptoms and EEG from a dimensional approach via multivariate regression. The current study also investigated whether the relation between ADHD symptoms and EEG

coherence was affected by sex. Based on prior literature, it was hypothesized that atypical coherence would predict higher scores of inattentiveness and hyperactivity, and that this relation would differ when considering sex.

Although previous research has investigated the relation between ADHD behavior symptoms and power and coherence measures of EEG, results of these studies have varied depending on the sex, age, and presentation of ADHD of the participants. Past coherence research in children with ADHD have found elevated intrahemispheric coherences in the frontal and central regions (Clarke et al., 2008b). Also, research has found that increased coherence of delta and theta bands exists in frontal region of the brain (Barry et al. 2002; Clarke et al., 2008b). Intrahemispheric and interhemispheric difference in theta coherence have been found in children with ADHD compared to children without ADHD, and studies have found that children with ADHD have lower intrahemispheric alpha coherences at longer inter-electrode distances than children

without ADHD (Barry et al., 2005; Barry et al., 2002; Clarke et al., 2005). Also, the research has shown that interhemispheric coherences in the alpha band are lower in children with ADHD when compared to children without ADHD (Clarke et al., 2005). In the only existing study on coherence in adults with ADHD, adults with ADHD had lower alpha coherence in the short-medium, inter-electrode distances (Clarke et al., 2008b). However, no studies have exclusively looked at the correlation of ADHD

symptomatology and EEG coherence in college students while considering sex as a covariate.

Overall, the findings of the current study support both hypotheses that atypical EEG coherence values predict more symptoms of inattentiveness and hyperactivity in college students with sex as a covariate. Specifically, the results indicate that significant associations exist between both EEG coherence and hyperactive symptoms, and EEG coherence and inattentive symptoms according to the CSS behavior rating scale.

However, this claim is limited to particular regions and frequencies because not all of the relations between coherence and ADHD symptoms were significant. Also, because the present study is the first of its kind to compare ADHD symptoms to EEG from a

dimensional approach, it is not possible to directly generalize findings to previous studies that separated groups into ADHD and non-ADHD. Therefore, interpretations were made with caution, and the CSS self-reports of high symptoms of inattentiveness and

hyperactivity were tentatively compared with ADHD diagnoses in previous studies to draw some parallels between the current study’s findings and previous research.

When including sex in the model as a covariate, lower temporal beta

and lower temporal theta interhemisphic coherence scores significantly predicted higher CSS scores for inattentiveness. This finding supports the theory that beta coherence typically decreases with age due to the typical reduction in hyperactivity with development (Bellak & Black, 1991; Clarke et al., 2008b). Also, prior studies have indicated that high theta power is associated with higher symptoms of adult

inattentiveness in ADHD (Loo & Barkley, 2005), but this has not been considered in terms of coherence. Therefore, the finding of the current study that inattentive ADHD symptoms negatively predict temporal interhemispheric theta coherence needs to be replicated in future studies. Both of these significant findings in the multivariate

regression model with sex as a covariate provide support for the hypothesis that atypical coherence is associated with higher CSS inattentiveness scores.

The exploratory analyses examining the association between coherence and inattentiveness and hyperactivity in males and females in separate models led to additional significant associations between ADHD symptoms and EEG coherence.

However, due to the exploratory nature of these analyses, the significant results should be interpreted with caution and must be further tested in confirmatory studies to determine if true results exist (Bender & Lange, 2001). This is supported by the previous studies that have included separate analyses for males and females with ADHD (Wolterling et al., 2012; Koehler et al., 2009; Bresnahan, 2002; Bresnahan & Barry, 2002; Skirrow et al., 2013). Despite that there were only 14 males compared to 28 females, more statistically significant relations were found between the male CSS data and coherences. Specifically, in terms of the relation between coherence and hyperactive symptoms, higher left alpha short/medium intrahemispheric coherences (Fp1-F3, T3-T5, C3-P3) significantly

predicted higher scores of hyperactivity, and higher right alpha short/medium intrahemispheric coherences (FP2-F4, T4-T6, C4-P 4) significantly predicted higher scores of hyperactivity in males. This relation has not been reported in prior literature; however, Clarke and colleagues (2008b) interpreted reduced alpha coherence as an association with inattention. The current study’s results support that higher central, temporal, and frontal alpha coherences in both the left and right hemispheres are associated with hyperactive symptoms, suggesting that more research is needed to determine the relation between alpha coherence and ADHD symptoms. Due to the current study’s approach to ADHD symptoms dimensionally, it is possible that these analyses captured a unique association between central, temporal, and frontal alpha coherence and strictly hyperactive behaviors that could be overshadowed by a diagnosis of ADHD, which is typically represented by reduced hyperactive behaviors with older age. This association needs to be replicated in future studies.

In terms of the association between coherence and inattentive symptoms, higher left and right theta short/medium intrahemispheric coherences significantly predicted higher scores of inattention. The finding that more theta coherence in both the left and right central, temporal, and frontal regions is associated with more inattentive symptoms, which is consistent with the prior finding that adults with ADHD typically show more theta power activity than adults without ADHD (Koehler et al., 2009; Bresnahan, 2002; Bresnahan & Barry, 2002; Skirrow et al., 2013). While these studies have claimed that higher theta activity in adults with ADHD may represent impulsivity, the current findings suggest that specifically higher theta coherence may contribute to symptoms of

The results also indicated that higher left delta short/medium intrahemispheric coherences significantly predicted higher scores of inattention; and higher left delta long (F3-O1) intrahemispheric coherence significantly predicted higher scores of inattention. These findings that more delta coherence within the left hemisphere predict higher inattentive symptoms differ from Clarke and colleagues (2008) findings that that adult males with ADHD exhibited reduced hemispheric differences in delta coherence.

Therefore, the findings of the current study could be specific to age or strictly inattentive behaviors instead of ADHD. More research is needed to support this claim.

Significant relations between interhemispheric coherences and ADHD symptoms also were found. First, lower frontal beta interhemispheric (Fp1-Fp2, F7-F8, F3-F4) coherences significantly predicted higher scores of CSS hyperactivity, and lower central/parietal/occipital beta interhemispheric coherences (C3-C4, P3-P4, O1-O2) significantly predicted higher CSS inattention scores in males. These findings suggest that less beta coherence activity across hemispheres predicts atypical scores of

hyperactivity and inattention, depending on the region of the brain. These findings differ from previous literature that has found that beta coherence typically normalizes with age (Koehler et al., 2009; Bresnahan, 2002; Bresnahan & Barry, 2002; Skirrow et al., 2013). Because the sample consisted of young adults, the participants’ brains had not reached the maturational time point in which beta coherence normalizes. However, in children, more interhemispheric beta coherence is typically associated with ADHD symptoms, not less (Barry et al., 2002, 2005; Clarke et al, 2007; Barry et al., 2011). Therefore, a

Also, lower frontal alpha interhemispheric coherences (Fp1-Fp2, F7-F8, F3-F4) significantly predicted higher CSS hyperactivity scores in males. This is supported by Clarke and colleagues’ (2008) findings that individuals with ADHD had less alpha coherence than those without ADHD. However, the researchers claimed that this was due to reduced hyperactive symptoms and the presence of strictly inattentive symptoms. Therefore, the current findings should be replicated to determine whether the relation in the Clarke and colleagues (2008b) study is due to their claim.

Fewer significant relations were found between ADHD symptoms and EEG coherence in females than were found in males. This is consistent with previous literature suggesting that EEG differences between female participants with and without ADHD are subtler and less consistent in female participants than in males (Barry et al., 2006; Dupuy et al., 2008; Dupuy et al., 2012). Additionally, because 28 females were included in the study compared to the 14 males, their associations between ADHD symptoms and EEG coherence may have overshadowed the influence from the males, seemingly reducing the number of significant relations between ADHD symptoms and EEG coherence when sex was included in the model as a variable.

Two significant results were found between EEG coherence and ADHD symptoms within females. Specifically, lower delta central/parietal/occipital interhemispheric coherences predicted higher scores of inattention, and lower delta central/parietal/occipital interhemispheric coherences predicted higher scores of

hyperactivity. Because no studies have examined the relation between female coherence and ADHD before, this is a new finding in literature, supporting the current study’s

hypothesis that atypical coherence predicts ADHD symptoms differently in males and females.

Limitations

Despite the significant findings, there are several limitations to the current study. First, factors such as previous diagnoses, medication, and cognitive abilities were not controlled for, despite the fact that all of these have neurological origins. The sample size was also relatively small, especially for the male participants. In order to achieve a more accurate picture of the relation between coherence and ADHD symptoms, future studies should attempt to age match equal number of male to female participants in order to increase control. Also, only the CSS self-report was used to measure the ADHD symptoms in the participants. While this measure is supported empirically, other measures like the CAARS are often preferred as self-report screeners. Furthermore, because this is the first study of its kind to examine the dimensional relation between EEG coherence and ADHD symptoms according to a self-report in both males and females, there is a need to replicate these findings. Additionally, employing a moderated multiple regression approach in future studies instead of separating analyses for males and females would provide more statistical power.

Implications

The results of the current study suggest that EEG coherence can predict some differences in ADHD symptoms from a dimensional perspective in male and female young adults, thus providing tentative evidence that EEG may be useful as a

supplemental diagnostic tool for ADHD in college students. Specifically, EEG may act as a useful supplemental tool in assessment of college students since current assessment

practice heavily relies on self-report methods of ADHD symptoms and there is limited access to information from additional informants. Furthermore, these findings can be used to determine associations between atypical brain wave activity and subthreshold ADHD symptoms, acting as a tool that can be used to inform preventative services. For example, if an individual reports at-risk amounts of ADHD symptoms that are correlated with qEEG coherence, a clinician would have two sources of evidence suggesting that the patient may benefit from coping strategies for ADHD such as integrating structure into daily routine. The current findings also provide support for previous literature that has analyzed males and females with ADHD separately due to the sex differences in EEG caused by differential underlying brainwave activity. Overall, this study presents EEG as a potential supplemental tool in the future of assessment of college students with ADHD due to its ability to distinguish differences in ADHD symptoms across a dimensional scale.

REFERENCES

American Academy of Pediatrics (AAP) (2000). Clinical practice guideline: diagnosis and evaluation of the child with attention-deficit/hyperactivity disorder.

Pediatrics, 105(5), 1158–1170.

Advokat, C. (2009). What exactly are the benefits of stimulants for ADHD?. Journal of Attention Disorders, 12(6), 495-498.

Advokat, C., Lane, S. M., & Luo, C. (2011). College students with and without ADHD: Comparison of self-report of medication usage, study habits, and academic achievement. Journal of Attention Disorders, 15(8), 656-666.

Alexander, L., & Liljequist, L. (2016). Determining the accuracy of self-report versus informant-report using the Conners’ Adult ADHD rating scale. Journal of Attention Disorders, 20(4), 346-352.

American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

Arns, M., Conners, C. K., & Kraemer, H. C. (2013). A decade of EEG theta/beta ratio research in ADHD: a meta-analysis. Journal of Attention Disorders, 17 (5), 374–

383.

Arns, M., Gunkelman, J., Breteler, M., & Spronk, D. (2008). EEG phenotypes predict treatment outcome to stimulants in children with ADHD. Journal of Integrative Neuroscience, 7(03), 421-438.

Arnsten, A. F. (2006). Stimulants: Therapeutic actions in ADHD.

Neuropsychopharmacology, 31(11), 2376-2383.

Baehne, C. G., & Fallgatter, A. J. (2009). Neurophysiology of adult attention-deficit hyperactivity disorder. Attention-Deficit Hyperactivity Disorder (ADHD) in Adults,176(1), 78-87.

Balázs, J., & Keresztény, Á. (2014). Subthreshold attention deficit hyperactivity in children and adolescents: a systematic review. European Child & Adolescent Psychiatry, 23(6), 393-408.

Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65.

Barkley, R.A. (1998). Attention Deficit Hyperactivity Disorder: A handbook for diagnosis and treatment (2nd Ed.).New York: Guilford Press.

Barkley, R. A. (Ed.). (2014). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment. Guilford Publications. P 62.

Barkley, R. A., Fischer, M., Smallish, L., & Fletcher, K. (2002). The persistence of attention-deficit/hyperactivity disorder into young adulthood as a function of reporting source and definition of disorder. Journal of Abnormal

Psychology, 111(2), 279.

Barkley, R.A., & Murphy, K.R. (2006). Attention-Deficit Hyperactivity Disorder: A Clinical Workbook. New York: Guilford Press.

Barry, R. J., Clarke, A. R., Hajos, M., Dupuy, F. E., McCarthy, R., & Selikowitz, M. (2011). EEG coherence and symptom profiles of children with attention- deficit/hyperactivity disorder. Clinical Neurophysiology, 122(7), 1327-1332.

Barry, R. J., Clarke, A. R., & Johnstone, S. J. (2003). A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative

electroencephalography. Clinical Neurophysiology, 114(2), 171-183.

Barry, R. J., Clarke, A. R., Johnstone, S. J., McCarthy, R., & Selikowitz, M. (2009). Electroencephalogram θ/β ratio and arousal in attention-deficit/hyperactivity

disorder: Evidence of independent processes. Biological Psychiatry, 66(4), 398-

401.

Barry, R. J., Clarke, A. R., McCarthy, R., & Selikowitz, M. (2002). EEG coherence in attention-deficit/hyperactivity disorder: a comparative study of two DSM-IV types. Clinical Neurophysiology, 113(4), 579-585.

Barry, R. J., Clarke, A. R., McCarthy, R., & Selikowitz, M. (2006). Age and gender effects in EEG coherence: III. Girls with attention-deficit/hyperactivity disorder. Clinical Neurophysiology, 117(2), 243-251.

Barry, R. J., Clarke, A. R., McCarthy, R., Selikowitz, M., Johnstone, S. J., Hsu, C. I., ... & Magee, C. A. (2005). Age and gender effects in EEG coherence: II. Boys with attention deficit/hyperactivity disorder. Clinical Neurophysiology, 116(4), 977-

984.

Barry, R., Clarke, A., McCarthy, R., Selikowitz, M., Johnstone, S., Rushby, J., 2004. Age and gender effects in EEG coherence: I. Developmental trends in normal children.

Clinical Neurophysiology.115(10), 2252–2258.

Baumgardner, T. L., Singer, H. S., Denckla, M. B., Rubin, M. A., Abrams, M. T., Colli, M. J., & Reiss, A. L. (1996). Corpus callosum morphology in children with

Tourette syndrome and attention deficit hyperactivity disorder. Neurology, 47(2),

477-482.

Bell, A. S. (2011). A critical review of ADHD diagnostic criteria: what to address in the DSM- V. Journal of Attention Disorders, 15(1), 3-10.

Bellak, L., & Black, R. B. (1991). Attention-deficit hyperactivity disorder in adults. Clinical Therapeutics, 14(2), 138-147.

Belsley, D. A., Kuh, E., & Welsch, R. E. (2005). Regression diagnostics: Identifying influential data and sources of collinearity (Vol. 571). John Wiley & Sons.

Bender, R., & Lange, S. (2001). Adjusting for multiple testing—when and how?. Journal of Clinical Epidemiology, 54(4), 343-349.

Benson, K., Flory, K., Humphreys, K. L., & Lee, S. S. (2015). Misuse of stimulant medication among college students: a comprehensive review and meta- analysis. Clinical Child and Family Psychology Review, 18(1), 50-76.

Berridge, C. W., Devilbiss, D. M., Andrzejewski, M. E., Arnsten, A. F., Kelley, A. E., Schmeichel, B., ... & Spencer, R. C. (2006). Methylphenidate preferentially increases catecholamine neurotransmission within the prefrontal cortex at low doses that enhance cognitive function. Biological Psychiatry, 60(10), 1111-1120.

Biederman, J., Mick, E., & Faraone, S. V. (2000). Age-dependent decline of symptoms of attention deficit hyperactivity disorder: impact of remission definition and

symptom type. American Journal of Psychiatry, 157(5), 816-818.

Boutros, N., Fraenkel, L., & Feingold, A. (2005). A four-step approach for developing diagnostic tests in psychiatry: EEG in ADHD as a test case. The Journal of Neuropsychiatry and Clinical Neurosciences, 17(4), 455-464.

Bresnahan, S. M., Anderson, J. W., & Barry, R. J. (1999). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biological Psychiatry, 46(12), 1690-1697.

Bresnahan, S. M., & Barry, R. J. (2002). Specificity of quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Research, 112(2),

133–144. doi:10.1016/S0165-1781(02)00190-7

Brown, A. J., & Casey, B. M. (2016). Subclinical ADHD-symptoms are associated with executive-functioning and externalizing problems in college students without ADHD-diagnoses. Journal of Educational and Developmental Psychology, 6(1),

204.

Bush, G. (2011). Cingulate, frontal, and parietal cortical dysfunction in attention deficit/hyperactivity disorder. Biological Psychiatry, 69(12), 1160-1167.

Calderon, O., & Ruben, L. (2008). A contextual, multidimensional, interdisciplinary approach to assessment of ADHD. Best Practices in Mental Health, 4(2), 59-79.

Callaway, E., Halliday, R., & Naylor, H. (1983). Hyperactive children's event-related potentials fail to support underarousal and maturational-lag theories. Archives of General Psychiatry, 40(11), 1243-1248.

Carragher, N., Krueger, R. F., Eaton, N. R., Markon, K. E., Keyes, K. M., Blanco, C., ... & Hasin, D. S. (2014). ADHD and the externalizing spectrum: direct comparison of categorical, continuous, and hybrid models of liability in a nationally representative sample. Social Psychiatry and Psychiatric Epidemiology, 49(8),

Castellanos, F. X., Giedd, J. N., Eckburg, P., Marsh, W. L., Vaituzis, A. C., Kaysen, D., ... & Rapoport, J. L. (1994). Quantitative morphology of the caudate nucleus in attention deficit hyperactivity disorder. American Journal of Psychiatry, 151(12),

1791-1796.

Castellanos, F. X., & Hyde, Z. (2010). Diffusion tensor imaging provides new clues in adults with ADHD (Commentary on Konrad et al.). European Journal of Neuroscience, 31(5), 910-911.

Castellanos, F. X., Lee, P. P., Sharp, W., Jeffries, N. O., Greenstein, D. K., Clasen, L. S., ... & Zijdenbos, A. (2002). Developmental trajectories of brain volume

abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. Jama, 288(14), 1740-1748.

Centers for Disease Control and Prevention (CDC) (2005). Mental health in the United States. Prevalence of diagnosis and medication treatment for attention-

deficit/hyperactivity disorder--United States, 2003. MMWR. Morbidity and Mortality Weekly Report, 54(34), 842.

Chabot, R. J., & Serfontein, G. (1996). Quantitative electroencephalographic profiles of children with attention deficit disorder. Biological psychiatry,40(10), 951-963.

Chatterjee, S., & Hadi, A. S. (1986). Influential observations, high leverage points, and outliers in linear regression. Statistical Science, 379-393.

Clarke, A. R., Barry, R. J., Bond, D., McCarthy, R., & Selikowitz, M. (2002). Effects of stimulant medications on the EEG of children with attention-deficit/hyperactivity disorder. Psychopharmacology, 164(3), 277-284.

Clarke, A. R., Barry, R. J., Heaven, P. C., McCarthy, R., Selikowitz, M., & Byrne, M. K.